Comparative effectiveness research (CER) relies fundamentally on accurate assessment of clinical outcomes. The growing number of assessment instruments, as well as the rapid escalation in the cost has generated the increasing need for scientifically rigorous comparisons of the diagnostic tests in clinical practic via meta-analysis. Meta-analysis of diagnostic tests presents many additional statistical challenges compared to traditional meta-analysis of randomized clinical trials. In particular, diagnostic accuracy cannot be adequately summarized by a single measure; paired measures are typically used, for example, most popularly sensitivity (Se) and specificity (Sp), or alternatively positive and negative predictive values; and either of the paired measures is typically correlated. Furthermore, those diagnostic accuracy measures may depend on disease prevalence and in many studies, the reference standard is also subject to measurement error. In response to PAR-10-168, the overall goal of this proposal is to develop cutting-edge multivariate meta- analysis methods of diagnostic tests, and to integrate them into publicly available, easy-to-use R software package meta to enhance the consistency, applicability, and generalizability. In this proposal, we will focus on: (1) developing multivariate methods and software to efficiently detect and adjust for publication bias and other sample size effects in meta-analysis of diagnostic tests; and (2) developing network meta-analysis framework and software to simultaneously compare multiple diagnostic tests. We propose to perform empirical assessment of the strengths and weaknesses of these methods through real data applications and simulations. The proposed statistical methodology will be broadly applicable to the meta-analysis comparing diagnostic tests. It will improve public health by facilitating the diagnosis of various cancers, cardiovascular, infectious and other diseases. Completion of these two aims will directly benefit the CER program by providing state-of-the art methods implemented in user-friendly software using the WinBUGS and R statistical languages that will be made freely available to the public.